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A publication of ISCA*: International Society for Computers and Their Applications INTERNATIONAL JOURNAL OF COMPUTERS AND THEIR APPLICATIONS TABLE OF CONTENTS Page Guest Editorial: Selected Papers from CATA 2018 . 103 Gordon Lee and Les Miller Labeling Methods Using EEG to Predict Drowsy Driving with Facial Expression Recognition Technology . 104 Daichi Naito, Ryo Hatano, and Hiroyuki Nishiyama LMS Performance Issues: A Case Study of D2L . 113 Sourish Roy, Carey Williamson, and Rachel McLean An Evaluation of the Performance of Join Core and Join Indices Query Processing Methods . 123 Reham M. Almutairi, Mohammed Hamdi, Feng Yu, and Wen-Chi Hou Novel Low Latency Load Shared Multicore Multicasting schemes – An Extension to Core Migation . 132 Bidyut Gupta, Ashraf Alyanbaawi, Nick Rahimi, Koushik Sinha, and Ziping Liu * “International Journal of Computers and Their Applications is abstracted and indexed in INSPEC and Scopus.” Volume 25, No. 3, Sept. 2018 ISSN 1076-5204 International Journal of Computers and Their Applications A publication of the International Society for Computers and Their Applications EDITOR-IN-CHIEF Dr. Frederick C. Harris, Jr., Professor Department of Computer Science and Engineering University of Nevada, Reno, NV 89557, USA Phone: 775-784-6571, Fax: 775-784-1877 Email: [email protected], Web: http://www.cse.unr.edu/~fredh ASSOCIATE EDITORS Dr. Hisham Al-Mubaid Dr. Wen-Chi Hou Dr. James E. Smith University of Houston-Clear Lake, Southern Illinois University, USA West Virginia University, USA USA [email protected] [email protected] [email protected] Dr. Shamik Sural Dr. Ramesh K. Karne Dr. Antoine Bossard Indian Institute of Technology Towson University, USA Advanced Institute of Industrial Kharagpur, India [email protected] [email protected] Technology, Tokyo, Japan [email protected] Dr. Ramalingam Sridhar Dr. Bruce M. McMillin The State University of New York at Dr. Mark Burgin Missouri University of Science and Buffalo, USA University of California, Technology, USA [email protected] Los Angeles, USA [email protected] [email protected] Dr. Junping Sun Nova Southeastern University, USA Dr. Muhanna Muhanna Dr. Sergiu Dascalu [email protected] Princess Sumaya University for University of Nevada, USA Technology, Amman, Jordan [email protected] [email protected] Dr. Jianwu Wang University of California Dr. Sami Fadali San Diego, USA University of Nevada, USA Dr. Mehdi O. Owrang [email protected] [email protected] The American University, USA [email protected] Dr. Yiu-Kwong Wong Dr. Vic Grout Hong Kong Polytechnic University, Glyndŵr University, Hong Kong Dr. Xing Qiu Wrexham, UK [email protected] University of Rochester, USA [email protected] [email protected] Dr. Yi Maggie Guo Dr. Rong Zhao University of Michigan, Dr. Abdelmounaam Rezgui The State University of New York Dearborn, USA New Mexico Tech, USA at Stony Brook, USA [email protected] [email protected] [email protected] ISCA Headquarters…•…P. O. Box 1124, Winona, MN 55987 USA…•…Phone: (507) 458-4517 E-mail: [email protected] • URL: http://[email protected]. Copyright © 2018 by the International Society for Computers and Their Applications (ISCA) All rights reserved. Reproduction in any form without the written consent of ISCA is prohibited. IJCA, Vol. 25, No. 2, Sept. 2018 103 Guest Editorial: Selected Papers from CATA 2018 CATA (Computers and their Applications) is one of the flagship conferences for the International Society of Computers and their Applications. The intent of this conference has been to blend theory and practice as a means of stimulating researchers from both research dimensions. The papers for this special issue are extensions of their conference version and illustrate the spectrum of the 38 papers presented at the CATA 2018 conference. Paper 1: Labeling Method Using EEG to Predict Drowsy Driving with Facial Expression Recognition Technology. Authors: Daichi Naito, Ryo Hatano and Hiroyuki Nishiyama. In this paper, the authors develop a non-contact method for predicting a driver’s drowsiness based on a set of classifiers that make use of a combination of EEG readings and facial expression capture for the same time period. They use a 3D camera and employ several machine learning methods as well as automated labeling for prediction. The Gaussian kernel SVM method gave the best performance and results show that their approach provides a viable and efficient approach for predicting a driver’s drowsiness. Paper 2: LMS Performance Issues: A Case Study of D2L. Authors: Sourish Roy, Carey Williamson, and Rachel McLean. The paper focuses on a particular network, the Learning Management System at the University of Calgary, and the authors perform a study of the traffic flow, identify the causes for traffic congestion, offer several solutions to the congestion problem, and present a web cache simulation study to show that this simple solution alleviates traffic problems. Paper 3: Performance Evaluations of Two Fast Join Query Processing Methods: Join Core and Join Indices. Authors: Reham M. Almutairi, Mohammed Hamdi, Feng Yu, and Wen-Chi Hou. An evaluation of the performance of the relational database model is provided in this paper. In particular, the authors compared the performance of the Jive-join algorithm, the Join Core algorithm and the MySQL implementation. It is shown that the join core performs faster that the join indices, although both methods perform well for TCP-H benchmark datasets. Paper 4: Novel Low Latency Load Shared Multicore Multicasting Schemes – An Extension to Core Migration. Authors: Bidyut Gupta, Ashraf Alyanbaawi, Koushik Sinha, and Nick Rahimi. The authors present four schemes for load shared multicasting using static networks. Two of the methods consider load sharing as the performance metric, while the other two also focus on low latency multicasting. Further, the authors discuss the feasibility of these strategies. Gordon Lee (Program Chair, CATA 2018) and Les Miller (General Chair, CATA 2018) ISCA Copyright© 2018 104 IJCA, Vol. 25, No. 2, Sept. 2018 Labeling Method Using EEG to Predict Drowsy Driving with Facial Expression Recognition Technology Daichi Naito*, Ryo Hatano*, and Hiroyuki Nishiyama* Tokyo University of Science, Yamazaki 2641, Noda-shi, CHIBA, 278-8510, JAPAN Abstract focus on predicting its occurrence. Much of the literature on drowsy driving prediction deals with the driver’s pulse, Accidental death caused by driver negligence is a serious electroencephalogram (EEG), and facial expressions, among problem, and the most common cause of such accidents is other indicators [3]. To predict drowsy driving using an EEG careless driving, in particular, driving while drowsy. Drowsy or pulse, it is necessary to attach a device to the driver’s body, driving can be caused by overwork and may lead to serious and this may disturb their concentration while driving, making accidents. Studies have proposed using an the use of such attachable devices to predict drowsy driving electroencephalogram (EEG), pulse, and facial expressions to impractical or undesirable. predict drowsy driving. Predicting drowsy driving using an We therefore aim to predict drowsy driving with a non-contact EEG or pulse, however, may require the driver to wear approach. To that end, we record drivers’ facial expressions cumbersome devices, which is likely to disturb their using a 3D camera, and predict drowsy driving with machine concentration while driving. We therefore aim to predict learning. However, developing an effective machine learning drowsy driving from a driver's facial expressions in a non- model requires a considerable amount of time and effort, as it is contact manner. In particular, we record facial expressions necessary for humans to watch many videos of drivers to with a 3D camera, and predict drowsy driving using machine categorize (label) the training data. This makes it difficult to learning. This, however, requires visually checking the prepare enough training data to obtain a generalized prediction driver’s state, to label the data, which is time consuming and model, an issue which is probably also similar for other studies makes it difficult to prepare sufficient labeled data for machine that employ an approach similar to ours. To tackle this learning. In this study, we propose an automated method for problem, we propose a new labeling approach, using EEG data labeling, using a machine learning technique based on EEG data. to shorten the time required for labeling, as it is known that an Evaluation of the proposed method is conducted, based on EEG is helpful for determining the level of sleepiness [1, 6]. accuracy and the F-value, for unknown data prediction. We organize the rest of this paper as follows: Section 2 Key Words: Drowsy, driving, machine learning, labeling, summarizes several studies with respect to sleeping and drowsy EEG, facial expressions. driving; Section 3 presents an overview of our method and the principles of machine learning; Section 4 shows the 1 Introduction experimental environment and the analysis of our results; and Section 5 concludes this paper with further remarks. Deaths caused by accidents resulting from driver negligence have become a serious problem in recent years. Figure 1 2 Related Works shows the yearly trend in causes of fatal accidents due to driver negligence in Japan [7]. The Metropolitan Police Department Nishiyama et al. [5] proposed a method to predict drowsy Transportation Bureau reports that the most common cause of driving using drivers’ facial expression data, obtained with a 3D accidental death from driver negligence is careless driving. It camera. They applied inductive logic programming to the data, is therefore seen as being greatly beneficial if the rate of careless and obtained a set of rules that could be interpreted as driving could be reduced. corresponding formulas of first-order logic. The obtained “Careless driving” refers to situations in which the driver is rules involved expressions that allowed for prediction of drowsy unable to concentrate on driving – such as driving when drowsy.